131 lines
3.9 KiB
Python
131 lines
3.9 KiB
Python
from flask import Flask, request, jsonify
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import torch
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from transformers import BertTokenizer, BertForSequenceClassification
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import joblib
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import re
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from functools import lru_cache
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from concurrent.futures import ThreadPoolExecutor
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from typing import List, Dict
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import threading
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# 配置参数
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MAX_LENGTH = 512
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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SAVE_DIR = "cate1" # 模型路径
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LABEL_ENCODER_PATH = "cate1/label_encoder.pkl" # 标签映射器路径
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app = Flask(__name__)
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app.config['JSON_AS_ASCII'] = False
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# 全局变量锁
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model_lock = threading.Lock()
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class Predictor:
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"""预测器类，用于管理模型和分词器的生命周期"""
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_instance = None
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def __new__(cls):
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if cls._instance is None:
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with model_lock:
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if cls._instance is None: # 双重检查锁定
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cls._instance = super().__new__(cls)
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cls._instance._initialized = False
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return cls._instance
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def __init__(self):
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if self._initialized:
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return
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# 加载模型和分词器
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self.tokenizer = BertTokenizer.from_pretrained(SAVE_DIR)
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self.model = BertForSequenceClassification.from_pretrained(SAVE_DIR).to(DEVICE)
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self.model.eval()
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# 加载标签映射器
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self.label_encoder = joblib.load(LABEL_ENCODER_PATH)
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self.label_map = {i: label for i, label in enumerate(self.label_encoder.classes_)}
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# 创建线程池
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self.executor = ThreadPoolExecutor(max_workers=4)
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self._initialized = True
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@lru_cache(maxsize=1000)
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def clean_text(self, text: str) -> str:
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"""文本清洗函数，带缓存"""
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if not isinstance(text, str):
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return ""
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cleaned_text = re.sub(r'[^\u4e00-\u9fa5]', '', text)
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return cleaned_text.strip()
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def predict_single(self, text: str) -> Dict:
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"""单个文本预测"""
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with torch.no_grad():
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cleaned_text = self.clean_text(text)
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if not cleaned_text:
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return {"category": "", "confidence": 0.0}
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inputs = self.tokenizer(
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cleaned_text,
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return_tensors="pt",
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truncation=True,
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padding=True,
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max_length=MAX_LENGTH
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).to(DEVICE)
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outputs = self.model(**inputs)
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probs = torch.softmax(outputs.logits, dim=1).cpu()
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top_prob, top_idx = torch.topk(probs, k=1)
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#去掉置信度，速度提升10%-30%
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# top_idx = torch.argmax(outputs.logits, dim=1)
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return {
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"category": self.label_map[top_idx.item()],
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"confidence": top_prob.item()
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}
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def batch_predict(self, texts: List[str]) -> List[Dict]:
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"""批量预测（并发处理）"""
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if not texts:
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return []
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# 使用线程池并发处理
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futures = [self.executor.submit(self.predict_single, text) for text in texts]
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return [future.result() for future in futures]
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# 初始化预测器
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predictor = Predictor()
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@app.route('/predict', methods=['POST'])
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def predict():
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"""预测接口"""
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data = request.get_json()
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if not data or 'product_names' not in data:
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return jsonify({"error": "Invalid request, 'product_names' is required"}), 400
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product_names = data['product_names']
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if not isinstance(product_names, list):
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product_names = [product_names]
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# 批量预测
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results = predictor.batch_predict(product_names)
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return jsonify({
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"status": "success",
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"predictions": results
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})
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@app.route('/health', methods=['GET'])
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def health_check():
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"""健康检查接口"""
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return jsonify({"status": "healthy"})
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=5002, threaded=True)
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else:
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application = app # 兼容 WSGI 标准（如 Gunicorn）
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